Supervised k-means clustering is used here. Again the goal is NOT to determine the number of row or column clusters here.

#Use the top5K features (genes) which normalized by its mean value for each row and visualize as a Heatmap
#Kmeans here
Heatmap(TCGA_only_f1_prot_QN_l2_m_5K,
        col = colorRamp2(c(-2, 0, 2),c("purple", "#EEEEEE", "yellow")),
        show_column_names = F, show_row_names = F,
        cluster_columns = T,cluster_rows = T,
        show_column_dend = T, show_row_dend = T,
        clustering_distance_rows = "pearson", clustering_distance_columns = "pearson",
        show_heatmap_legend =T,
        row_km = 8, column_km = 4
        )

Unsupervised Hierarchical clustering is used here

#Hierarchical and unsupervised clustering
Heatmap(TCGA_only_f1_prot_QN_l2_m_5K,
        col = colorRamp2(c(-2, 0, 2),c("blue", "#EEEEEE", "orange")),
        show_column_names = F, show_row_names = F,
        cluster_columns = T,cluster_rows = T,
        show_column_dend = T, show_row_dend = T,
        clustering_distance_rows = "pearson", clustering_distance_columns = "pearson",
        show_heatmap_legend =T
        )

Kmeans clustering and PCA plot to visualize patient segments

PLOTPCA_PRCOMP=function(inputM,clus) {
    data_in=t(
                inputM
                )
    res.pca=prcomp(data_in,
                    center = TRUE,scale. = TRUE)
    df_data_in=as.data.frame(data_in)
    df_data_in$clus=clus
    #Now plot PCA
    autoplot(res.pca,data =df_data_in,colour="clus",label=F,title=paste(topNgenes))
}

#As you can see colnames have changed from "-" to "."
colnames(TCGA_only_f1_prot_QN_l2_5K)[1:5]
## [1] "TCGA.BH.A0W3.01A.11R.A109.07" "TCGA.A8.A07F.01A.11R.A00Z.07"
## [3] "TCGA.GM.A3XL.01A.11R.A22U.07" "TCGA.LL.A441.01A.11R.A24H.07"
## [5] "TCGA.BH.A0HP.01A.12R.A084.07"
#So convert this according to this code:
colnms=unlist(lapply(colnames(TCGA_only_f1_prot_QN_l2_5K),function(x) paste((strsplit(x, "[.]"))[[1]],collapse="-") ) )
colnms[1:5]
## [1] "TCGA-BH-A0W3-01A-11R-A109-07" "TCGA-A8-A07F-01A-11R-A00Z-07"
## [3] "TCGA-GM-A3XL-01A-11R-A22U-07" "TCGA-LL-A441-01A-11R-A24H-07"
## [5] "TCGA-BH-A0HP-01A-12R-A084-07"
dtm=TCGA_only_f1_prot_QN_l2_5K
colnames(dtm)=colnms
        
KM_clus_cols=kmeans(t(dtm), 4)

#Notice that we are using 5,000 features. Hence the PC1 and PC2 (%) that can describe the data is small.
#As we can see there is not a reliable way to know the total number of patient segments in exploratory analysis
PLOTPCA_PRCOMP(inputM=dtm,clus = as.character(KM_clus_cols$cluster) )

Lets get some help from statistics to know the number of clusters

Consensus cluster package allows us to discover the robust number of segments in an unsupervised setting 

results = ConsensusClusterPlus(dtm,maxK=8,reps=100,pItem=0.8,pFeature=1,
                               clusterAlg="hc",distance="pearson",seed=25)
## end fraction
## clustered

## clustered

## clustered

## clustered

## clustered

## clustered

## clustered

This gives us a bunch of plots that shows how the cumulative distributive function and the area under the curve increases upto k=4 clusters beyond which it falls off; We can conclude that k=4 gives us robust number of clusters to work with going forward ## Use Kaplan Meier plot to define clinically relevant population segments

clinical_patient_Cancer <- GDCquery_clinic("TCGA-BRCA","clinical")
 #clinical_patient_Cancer <- data.frame(
 #     bcr_patient_barcode = substr(colnms,1,12)
 #)
 
clinical_patient_Cancer1=clinical_patient_Cancer
clinical_patient_Cancer1$clus=as.vector(KM_clus_cols$cluster)[
                                (match(clinical_patient_Cancer$submitter_id,substr(colnms,1,12)))
                                ]
 
 
TCGAanalyze_survival(clinical_patient_Cancer1,
                     clusterCol="clus",
                     risk.table = F,
                     xlim = c(100,6000),
                     conf.int = FALSE,
                     color = c("Dark2"),
                     filename = NULL
                     )

Since Cluster 4 does poor than Cluster 3. We can do statistics on genes (features) that is significantly associated with this cluster

To do this, we use Kaplan-meier (KM) survival analysis - a univariate analysis; Each gene at a time is systematically used to separate patients with high/low expression and compared against the two groups of clusters

tabSurvKM <- TCGAanalyze_SurvivalKM(clinical_patient_Cancer,
                                     dtm,
                                     Genelist = rownames(dtm),
                                     Survresult = FALSE,
                                     p.cut = 0.4,
                                     ThreshTop = 0.7,
                                     ThreshDown = 0.2,
                                     group1 = names(KM_clus_cols$cluster==3), 
                                     group2 = names(KM_clus_cols$cluster==4)
                                    ) 
## 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#Top 20 Genes - ranked by pvalue
tabSurvKM[1:20,]
##              pvalue Group2 Deaths Group2 Deaths with Top
## TFPI2  4.037315e-06            75                     34
## PARP3  8.595674e-05            76                     33
## SIM2   9.791153e-05            79                     62
## MRPL13 1.313109e-04            80                     63
## PLXNB3 1.821773e-04            71                     52
## DEF6   1.846113e-04            71                     29
## SIAH2  1.851016e-04            74                     31
## MFAP3L 1.894268e-04            74                     60
## NEFH   1.952606e-04            82                     38
## TUBA3D 2.678490e-04            74                     35
## KLRB1  2.986887e-04            78                     35
## NFKBIA 3.135293e-04            74                     36
## ME3    3.187074e-04            71                     32
## IL27RA 3.213773e-04            83                     41
## LRP11  3.739184e-04            79                     60
## PLXNB1 3.873892e-04            76                     33
## RELB   4.090616e-04            75                     36
## CCL19  4.156901e-04            80                     39
## RAC2   4.857662e-04            84                     41
## CEBPD  5.107309e-04            73                     34
##        Group2 Deaths with Down Mean Group2 Top Mean Group2 Down
## TFPI2                       41        3.424287        0.2513688
## PARP3                       43        3.823736        2.0910847
## SIM2                        17        2.074219        0.1461053
## MRPL13                      17        4.344877        2.6737357
## PLXNB3                      19        2.535805        0.6268989
## DEF6                        42        4.022412        2.2489408
## SIAH2                       43        6.403437        3.7991995
## MFAP3L                      14        2.360577        0.5295196
## NEFH                        44        2.317961        0.3503365
## TUBA3D                      39        3.015224        0.2535303
## KLRB1                       43        2.183702        0.4581276
## NFKBIA                      38        6.357202        4.8098374
## ME3                         39        2.849446        0.9279542
## IL27RA                      42        4.010303        1.5228941
## LRP11                       19        4.937458        3.2130290
## PLXNB1                      43        4.311172        2.5538674
## RELB                        39        3.850952        1.9828241
## CCL19                       41        5.855447        0.6904334
## RAC2                        43        4.898893        2.3746646
## CEBPD                       39        5.207401        2.2614666
##        Mean Group1
## TFPI2    1.5169547
## PARP3    3.0722441
## SIM2     0.9467238
## MRPL13   3.5341050
## PLXNB3   1.5928518
## DEF6     3.2027704
## SIAH2    5.1194388
## MFAP3L   1.3944473
## NEFH     1.1267307
## TUBA3D   1.3431554
## KLRB1    1.2644132
## NFKBIA   5.6487110
## ME3      1.8935418
## IL27RA   2.7661941
## LRP11    4.1122822
## PLXNB1   3.5167469
## RELB     2.9474765
## CCL19    3.3739565
## RAC2     3.7433060
## CEBPD    3.8423034